Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

Transcription

1 Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications PI: Prof. Lori Bruce, Ph.D Mississippi State University GeoResources Institute

2 GPM Optimization Team & MSU Team Lori Mann Bruce Valentine Anantharaj Jenny Du Nicholas Younan Collaborators Yangrong Ling Georgy Mostovoy Louis Wasson Graduate students External Collaborators Paul Houser (GMU CREW) Joe Turk (Naval Research Laboratories,Monterey, CA) Partner Agencies Garry Schaeffer (USDA NRCS) Steve Hunter (United States Bureau of Reclamation) 2

3 Team Activity MSUGRI: Precipitation merging & optimization, modeling, project management, and RPC Integration. NRL: GPM data, precipitation sensitivity analysis. GMU CREW: Ensemble Kalman Filter based optimal merging, downscaling, and science expertise. 3

4 Identified Decision Support Needs Routine analysis land surface state (soil moisture, evaporation, land surface temperature) over the continental involves: Observations water soils sun weather climate vegetation terrain Analysis / Modeling observe, model, assimilate Information 4

5 GPM Evaluations: Purpose and Activities 5

6 Purpose of RPC Experiment Optimize thegpm precipitation estimates for decision support in water resources managementand and othercross cutting cutting applications. Characterize and optimize GPMprecipitation data by blending and merging with other precipitation p measurements and estimates using a Four Dimensional Objective Analysis (4D OA) scheme and other intelligent methods. 6

7 Iterative Experimental Design 7

8 Experimental Objectives of GPM Optimization Develop dynamic 4D OA techniques (EnKF) and intelligent methods (ANN, Bayesian merging) to optimally merge various precipitation estimates. Evaluate and implement spatial downscaling and temporal disaggregation techniques to derive precipitation forcings for land surface modeling. Evaluate the optimized and downscaled products by running land surface model experiments at 1 10 km resolutions in selected domains. Characterize uncertainties in merged products and in LSM simulations. 8

9 Tasks to Achieve Objectives Precipitation Merging g ANN Method Feature Optimization Technique EnKFObjective Ananlysis Bayesian Merging Precipitation Downscaling Stochastical Physical Hybrid Method Hydrological Modeling Merged dforcings Downscaled forcings Analyze results and publish 9

10 Precipitation Datasets 10

11 Sources of Rainfall Data In situ gage observations RADAR measurements Estimates from satellite data Numerical Weather Prediction Models (NWP) 11

12 Example Precipitation Products Name Domain Space/Time Resolution GEOS 90 N 90 S, 180 W 180 E 1.25x hourly GDAS 90 N 90 S, 180 W 180 E ~ hourly EDAS North America 40 km 3 hourly RUC North America 40 km 3 hourly NRL IR 60 N 60 S, 180 W 180 E 0.25x hourly NRL MW 60 N 60 S, 180 W 180 E 0.25x hourly HUFFMAN 50 N 50 S, 180 W 180 E 0.25x hourly PERSIANN 50 N 50 S, 180 W 180 E 0.25x0.25 Hourly NEXRAD Continental US 4 km Hourly HIGGINS Continental US 0.25x Daily GTS 90 N 90 S, 180 W 180 E Station Data Daily CMAP 90 N 90 S, 180 W 180 E 2.50x2.50 Pentad CMORHP 60 N 60 S, 180 W 180 E 0.073x hourly 12

13 Satellite Omission Experiments 13

14 Motivation Precipitation is the main forcing for hydrological and land surface models Precipitation events that result in flooding often evolve over short space and time scales, where properly instrumented surface networks may not be available Satellite data often provides the only source of timely precipitation p data over many of the world s remote watersheds Hydrological modeling is a key focus of the future NASA/JAXA Global Precipitation Measurement Mission (GPM) in 2013; however The members of the GPM constellation will change owing to launch schedules before and during the mission; therefore How can we leverage today s existing environmental satellite constellation to examine the impact of (existing and future) satellites and orbits on hydrological applications? 14

15 Arkansas Red River Basins Arkansas Cimarron The Arkansas River is the longest tributary in the Mississippi Missouri system. From its source near Leadville, Colorado, the river travels through Kansas and northeastern Oklahoma. There it is joined by the Canadian, Cimarron, Neosho Grand, and Verdigris Rivers, crosses the stateofarkansaswhereitemptiesinto the Mississippi River. Canadian Red The Red River rises in two branches in the Texas Panhandle and flows east along the border of Texas and Oklahoma, and briefly between Texas and Arkansas. There it turns south into Louisiana to empty into the Atchafalaya and Mississippi Rivers. 15

16 Arkansas River Tributaries 16

17 Late June Oklahoma Heavy Rain Events and Effect on Runoff of Small Streams 34.35N 98.31W 34.17N 98.46W Cache Creek drainage area Conversion: 1 m 3 s 1 = 35 ft 3 s 1 17

18 Examining Impact of Satellite Type for the GPM Era Satellite Omission Experiments Case 0:All satellites included (baseline) Case 1:Omit all crosstrack sounders Case 2:Omit morning crosstrack sounders Case 3:Omit afternoon crosstrack sounders Case 4:OmitTRMM TMI and PRand Aqua Case 5:Omit TRMM PR only Case 6:Omit TRMM TMI only Case 7:Omit O ittrmm TMI and PR Case 8:Omit all morning satellites Case 9:Omit all afternoon satellites 18

19 Examining Impact of Satellite Type for the GPM Era Satellite Omission Experiments: Baseline 24 hour accumulations ending 2007/06/29 12Z 12 hour accumulations ending 2007/06/29 12Z 6 hour accumulations ending 2007/06/29 12Z 3 hour accumulations ending 2007/06/29 12Z 19

20 Examining Impact of Satellite Type for the GPM Era Satellite Omission Experiments: Case 2 24 hour accumulations ending 2007/06/29 12Z 12 hour accumulations ending 2007/06/29 12Z 6 hour accumulations ending 2007/06/29 12Z 3 hour accumulations ending 2007/06/29 12Z 20

21 3 Hour Accumulations: Omit Crosstrack Sounders OKLAHOMA (33N 38N 38N 100W 94W) TEXAS (28N 34N 102W 94W) AVERAGE WITHIN REGION PEAK WITHIN REGION Case 1: Case 2: Case 3: Omit all crosstrack sounders Omit morning crosstrack sounders Omit afternoon crosstrack sounders 21

22 6 Hour Accumulations: Omit Crosstrack Sounders OKLAHOMA (33N 38N 38N 100W 94W) TEXAS (28N 34N 102W 94W) AVERAGE WITHIN REGION PEAK WITHIN REGION Case 1: Case 2: Case 3: Omit all crosstrack sounders Omit morning crosstrack sounders Omit afternoon crosstrack sounders 22

23 12 Hour Accumulations: Omit Crosstrack Sounders OKLAHOMA (33N 38N 38N 100W 94W) TEXAS (28N 34N 102W 94W) AVERAGE WITHIN REGION PEAK WITHIN REGION Case 1: Case 2: Case 3: Omit all crosstrack sounders Omit morning crosstrack sounders Omit afternoon crosstrack sounders 23

24 24 Hour Accumulations: Omit Crosstrack Sounders OKLAHOMA (33N 38N 38N 100W 94W) TEXAS (28N 34N 102W 94W) AVERAGE WITHIN REGION PEAK WITHIN REGION Case 1: Case 2: Case 3: Omit all crosstrack sounders Omit morning crosstrack sounders Omit afternoon crosstrack sounders 24

25 3 Hour Accumulations: Omit TRMM/Aqua Imagers OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 4: Case 5: Case 6: Case 7: Omit TRMM TMI and PR and Aqua Omit TRMM PR only Omit TRMM TMI only Omit TRMM TMI and PR 25

26 6 Hour Accumulations: Omit TRMM/Aqua Imagers OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 4: Case 5: Case 6: Case 7: Omit TRMM TMI and PR and Aqua Omit TRMM PR only NASA Omit RPC TRMM Review (4/14/08) TMI only Omit TRMM TMI and PR 26

27 12 Hour Accumulations: Omit TRMM/Aqua Imagers OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 4: Case 5: Case 6: Case 7: Omit TRMM TMI and PR and Aqua Omit TRMM PR only Omit TRMM TMI only Omit TRMM TMI and PR 27

28 24 Hour Accumulations: Omit TRMM/Aqua Imagers OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 4: Case 5: Case 6: Case 7: Omit TRMM TMI and PR and Aqua Omit TRMM PR only Omit TRMM TMI only Omit TRMM TMI and PR 28

29 3 Hour Accumulations: Omit AM or PM Satellites OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 8: Omit all morning satellites Case 9: NASA Omit RPC all Review afternoon (4/14/08) satellites 29

30 6 Hour Accumulations: Omit AM or PM Satellites OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 8: Omit all morning satellites Case 9: NASA Omit RPC all Review afternoon (4/14/08) satellites 30

31 12 Hour Accumulations: Omit AM or PM Satellites OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 8: Omit all morning satellites Case 9: NASA Omit RPC all Review afternoon (4/14/08) satellites 31

32 24 Hour Accumulations: Omit AM or PM Satellites OKLAHOMA TEXAS AVERAGE PEAK VALUE Case 8: Omit all morning satellites Case 9: NASA Omit RPC all Review afternoon (4/14/08) satellites 32

33 SOE Initial Summary An intercomparison of GPM like satellite constellations was performed for the purpose of analyzing the impact of satellites and orbits on hydrological models Compared to the all satellites configuration, the elimination of the morning satellites (specifically the across track sounders) showed the largest impact However this type of experiment is not a true validation, where the different satellite configurations are compared against ground truth data Future means of validation (other than raingauge) are planned dthat t use other types of hd hydrological land land surface observations (runoff, fluxes) 33

34 Artificial Neural Networks Approach for Merging Multiple Precipitation Products

35 Research Objectives To combine precipitation related information from satellite estimates, model predictions, and rain gauge measurements in order to capitalize on the advantages of each product. To study the impact and sensitivity on land surface states of the finalprecipitation estimates. 35

36 Neural Network ANN Data Merging Multilayer Back Propagation Neural Network (BPNN) Training: Inputs: satellite estimates, model predictions, a bias term Output: gauge measurements The weights in the BPNN are used to adjust the errors. Nonlinear regression 36

37 ANN Data Merging (Cont d) Investigations to be conducted Is it reasonable to use gauge measurements as the desired doutputs t for the neural network ktraining? i When gauge measurements are unavailable, can the interpolated gauge measurements be used as the desired d outputs? If gauge measurements are considered to be noisy, how to modify the neural network training algorithm to accommodate the inaccuracy? Is there any other choice for the desired outputs? 37

38 ANN Data Merging (Cont d) Investigations to be conducted ( continued) What is the spatial scale for a specific neural network to remain effective (i.e., spatial generalization property)? What is the temporal scale for a specific neural network to remain effective (i.e., temporal generalization property)? In addition to the current existing unsupervised neural network based data merging approach, can a new unsupervised neural network be developed for precipitation data merging? 38

39 Merging Methods Weighted Sum M = w i D i i M: Merged output D i : ith data set w i : weight for the ith data set - e i : error in the ith data set w i = 1 ei ei 1 i 39

40 Merging Methods Least Squares Combination o T = w i D ˆ i M i = w i D i T: Data served as target during weight determination Dˆ i: ith data set used for weight determination M: Merged output D i : ith data set used for merging w i : weight for the ith data set estimated used a least squares approach i 40

41 CMORPH GMSRA GOES AE Merged Output ABRFC GOES HE SCAMPR Precipitation Data Merging Process for June 2007

42 Average Weighted Sum Least Squares Combination RMS Merged results for 6/16/2007 (12:00am)

43 Wavelet based Data Fusion Input Data: Precipitation observations from different satellites sources (products) Objective: To develop a fused data set which is better than individual data sets Fusion Tool: Redundant Wavelet Transform, Selection Rules 43

44 Intelligent Feature Optimization Precipitation Data Feature Optimization LIS Model Output D1 F1 G1 Precipitation Data Sets D2 Dn Feature Extraction F2 Fn Feature Reduction G2 Gn Eliminate Redundant Features FV Merged Precipitation Data Sets Eliminate Redundan t Features 44

45 Concept 45

46 Quality Map for GMSRA for June 46

47 Selection of valid data For any time series if the corresponding quality level is above three, it is used in the fusion process Otherwise the time series is discarded For instance, at a location (37,263) quality levels Dataset CMORPH GOES_AE GOES_HE QMORPH SCAMP R Quality level The time series to be fused are CMORPH, QMORPH, and SCAMPR. 47

48 Input Time Series and ABRFC 48

49 Fused Time Series 49

50 Evaluation Metrics Fusion Factor Correlation Coefficient Mean square error One class SVM distance measure Euclidean distance measure 50

51 Results Fusion Factor Data MWE SCC ADD PCA set Comparison between ABRFC and Merged Data Metric MWE SCC ADD PCA Corr Coeff MSE Euclidean Distance Bias %

52 Results (contd) CORRELATION COEFFICIENT BETWEEN OUTPUT AND INPUT Output CMORPH QMORPH SCAMPR MWE SCC ADD PCA BIAS BETWEEN INPUTS AND REF DATA DATA CMORPH QMORPH SCAMPR VALUE %

53 Results (contd) MSE BETWEEN OUTPUT AND INPUT Output Set1 Set2 Set3 MWE SCC ADD PCA SVM DISTANCE MEASURE WITH INPUT DATASETS Output MWE SCC ADD PCA Measure

54 Combined Physical and Statistical Approach for Downscaling Precipitation Products

55 A Hybrid Approach for Downscaling and Disaggregation of Precipitation Statistical Downscaling Physical Downscaling Hybrid bidapproach Stochastic downscaling in space Physical process based disaggregation in time Level 0 Level 1 Level 2 55

56 Space Time Downscaling Disaggregation 56

57 Downscaled Precipitation GCM equivalent Product Product Compared with ih Radar Observation 48 km 180 min Radar Observed Downscaled Product 3 km 10 min 3 km 10 min 57

58 Schedule Task ID Task Mar Dec 2007 Jan Jul 2008 Aug 08 Jun Develop and implement data merging technique 1.2 Test merged data in LIS Regional validation of optimized forcings 2.1 Develop downscaling methodology LIS control simulations at core sites 3.2 LIS simulations with different precipitation forcings 3.3 LIS validation against in-situ data 4.1 Document and report results 58

59 Contact Information Valentine Anantharaj edu> Tel: (662)